Simulation of congestion evolution in human-machine hybrid driving flow from the perspective of accident urgency
In order to improve the efficiency of accidental roadway access,a cellular automata model considering accident urgency was proposed.Firstly,a quantitative perception model of accident urgency between drivers of hand-driven vehicles(HVs)and connected and automated vehicles(CAVs)was constructed;secondly,the quantified accident urgency is incorporated into the lane-specific vehicle switching and following models,and the four impact scenarios of traffic flow caused by different driving behaviors such as deceleration following,lane changing,and acceleration departure are analyzed;finally,under different scenarios,simulation was used to verify the improvement effect of human-machine hybrid traffic flow organization in the accident section when vehicles were guided by the accident urgency.The results show that compared with the first scenario,the third impact scenario improves the total average operating speed by 37%,reduces the upstream vehicle congestion impact area by 75%,improves the maximum queue length by 10.56%,and improves the average queue length by 12.09%.
traffic safetythe flow of human-machine hybrid vehiclesevolution of traffic flowcellular automataemergency level of accidents
万方数据
维普
